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توضیحات

Distributed quantile sketches

ویژگی	مقدار
سیستم عامل	-
نام فایل	ddsketch-2.0.4
نام	ddsketch
نسخه کتابخانه	2.0.4
نگهدارنده	[]
ایمیل نگهدارنده	[]
نویسنده	Jee Rim, Charles-Philippe Masson, Homin Lee
ایمیل نویسنده	jee.rim@datadoghq.com, charles.masson@datadoghq.com, homin@datadoghq.com
آدرس صفحه اصلی	http://github.com/datadog/sketches-py
آدرس اینترنتی	https://pypi.org/project/ddsketch/
مجوز	-

# ddsketch This repo contains the Python implementation of the distributed quantile sketch algorithm DDSketch [1]. DDSketch has relative-error guarantees for any quantile q in [0, 1]. That is if the true value of the qth-quantile is `x` then DDSketch returns a value `y` such that `|x-y| / x < e` where `e` is the relative error parameter. (The default here is set to 0.01.) DDSketch is also fully mergeable, meaning that multiple sketches from distributed systems can be combined in a central node. Our default implementation, `DDSketch`, is guaranteed [1] to not grow too large in size for any data that can be described by a distribution whose tails are sub-exponential. We also provide implementations (`LogCollapsingLowestDenseDDSketch` and `LogCollapsingHighestDenseDDSketch`) where the q-quantile will be accurate up to the specified relative error for q that is not too small (or large). Concretely, the q-quantile will be accurate up to the specified relative error as long as it belongs to one of the `m` bins kept by the sketch. If the data is time in seconds, the default of `m = 2048` covers 80 microseconds to 1 year. ## Installation To install this package, run `pip install ddsketch`, or clone the repo and run `python setup.py install`. This package depends on `numpy` and `protobuf`. (The protobuf dependency can be removed if it's not applicable.) ## Usage ``` from ddsketch import DDSketch sketch = DDSketch() ``` Add values to the sketch ``` import numpy as np values = np.random.normal(size=500) for v in values: sketch.add(v) ``` Find the quantiles of `values` to within the relative error. ``` quantiles = [sketch.get_quantile_value(q) for q in [0.5, 0.75, 0.9, 1]] ``` Merge another `DDSketch` into `sketch`. ``` another_sketch = DDSketch() other_values = np.random.normal(size=500) for v in other_values: another_sketch.add(v) sketch.merge(another_sketch) ``` The quantiles of `values` concatenated with `other_values` are still accurate to within the relative error. ## Development To work on ddsketch a Python interpreter must be installed. It is recommended to use the provided development container (requires [docker](https://www.docker.com/)) which includes all the required Python interpreters. docker-compose run dev Or, if developing outside of docker then it is recommended to use a virtual environment: pip install virtualenv virtualenv --python=3 .venv source .venv/bin/activate ### Testing To run the tests install `riot`: pip install riot Replace the Python version with the interpreter(s) available. # Run tests with Python 3.9 riot run -p3.9 test ### Release notes New features, bug fixes, deprecations and other breaking changes must have release notes included. To generate a release note for the change: riot run reno new <short-description-of-change-no-spaces> Edit the generated file to include notes on the changes made in the commit/PR and add commit it. ### Formatting Format code with riot run fmt ### Type-checking Type checking is done with [mypy](http://mypy-lang.org/): riot run mypy ### Type-checking Lint the code with [flake8](https://flake8.pycqa.org/en/latest/): riot run flake8 ### Protobuf The protobuf is stored in the go repository: https://github.com/DataDog/sketches-go/blob/master/ddsketch/pb/ddsketch.proto Install the minimum required protoc and generate the Python code: ```sh docker run -v $PWD:/code -it ubuntu:18.04 /bin/bash apt update && apt install protobuf-compiler # default is 3.0.0 protoc --proto_path=ddsketch/pb/ --python_out=ddsketch/pb/ ddsketch/pb/ddsketch.proto ``` ### Releasing 1. Generate the release notes and use [`pandoc`](https://pandoc.org/) to format them for Github: ```bash git checkout master && git pull riot run -s reno report --no-show-source | pandoc -f rst -t gfm --wrap=none ``` Copy the output into a new release: https://github.com/DataDog/sketches-py/releases/new. 2. Enter a tag for the release (following [`semver`](https://semver.org)) (eg. `v1.1.3`, `v1.0.3`, `v1.2.0`). 3. Use the tag without the `v` as the title. 4. Save the release as a draft and pass the link to someone else to give a quick review. 5. If all looks good hit publish ## References [1] Charles Masson and Jee E Rim and Homin K. Lee. DDSketch: A fast and fully-mergeable quantile sketch with relative-error guarantees. PVLDB, 12(12): 2195-2205, 2019. (The code referenced in the paper, including our implementation of the the Greenwald-Khanna (GK) algorithm, can be found at: https://github.com/DataDog/sketches-py/releases/tag/v0.1 )

نیازمندی

مقدار	نام
-	six
-	typing
<4.21.0,>=3.0.0	protobuf
>=3.0.0	protobuf

زبان مورد نیاز

مقدار	نام
>=2.7	Python

نحوه نصب

نصب پکیج whl ddsketch-2.0.4:

pip install ddsketch-2.0.4.whl

نصب پکیج tar.gz ddsketch-2.0.4:

pip install ddsketch-2.0.4.tar.gz